Finding

walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-f

Walsh K, McGovern DP, Dully J, Kelly SP, O'Connell RG. Prior probability cues bias sensory encoding with increasing task exposure. eLife 12:RP91135, 2024. · Human visual contrast 2AFC keyboard task · psychometric

Observed curve and fits

Fit diagnostics

Variant AIC Δ AIC RMSE Caveats
logistic-4param winner 1699.6 0.0 0.0545 —
sdt-2afc 2634.0 934.4 0.1631 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -845.8 3,810 4 0.1492 12
sdt-2afc -1315.0 3,810 2 0.2475 12

Residuals

Fit parameters and provenance

logistic-4param.walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-f

dirty fit

bias=-5.674 · lower_lapse=0.056 · slope=0.081 · upper_lapse=0.058

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
12
BIC
1724.6

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.logistic-4param; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

sdt-2afc.walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-f

dirty fit

criterion=0.014 · d_prime=0.088

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
12
BIC
2646.5

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.sdt-2afc; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

Observed points

Signed target-distractor contrast p_right n
-26.000 0.1351 259
-25.000 0.0281 249
-20.000 0.0277 253
-10.000 0.0391 384
-7.000 0.0545 385
-6.000 0.0714 378
6.000 0.9314 379
7.000 0.9737 380
10.000 0.9816 380
20.000 0.9922 255
25.000 0.9524 252
26.000 0.7930 256

Take it with you

Cover sheet

Self-contained Markdown for citation, slides, or notebooks

The cover sheet pins the finding to the atlas commit and includes the observed points, fit ranking, caveats, and provenance — everything needed to drop into a paper or notebook without losing the trail back to the deploy.